![ImageNet Moment for Reinforcement Learning? [Prof. Jakob Foerster] @MachineLearningStreetTalk](https://i.ytimg.com/vi/Jr_nGkCG3og/maxresdefault.jpg)
Uploaded February 2025 | Updated September 2026, 1 week ago
SPONSOR MESSAGES:
***
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Prof. Jakob Foerster (FLAIR lab, Oxford / Meta AI) and his PhD student Chris Lu make the case that deep reinforcement learning is finally winning the hardware lottery. The core thesis: RL has underperformed not because the ideas are wrong, but because running environments on CPUs while training agents on GPUs created a computational bottleneck that made experimentation slow, expensive, and brittle. JAX-based GPU-native environments now deliver ~4000x speedups, enabling the kind of rapid iteration that made supervised deep learning successful.
Chris Lu explains the technical foundation -- how JAX's JIT compilation and vmap (vectorized map) allow writing a single environment instance in NumPy-like code and scaling it to millions of parallel copies on GPU. This started from necessity: their lab initially had only Google Colab free-tier compute. The constraint forced them to put environments on GPU for their Model-Free Opponent Shaping paper, and the results were surprisingly effective.
The conversation then shifts to discovered policy optimization. Foerster's mirror learning framework provides theoretical grounding for why PPO works, and crucially, lets you parameterize the drift function as a neural network and meta-learn it. Evolution strategies, not gradient-based meta-learning, turned out to be the better optimizer for this -- a vindication of the bitter lesson. The learned policy optimization function shows intriguing "too good to be true" behavior: when advantages are very high, it clips more aggressively, as if it has learned skepticism.
The second half covers multi-agent systems, emergent communication, and AI governance. Foerster argues forcefully for open-source AI development and democratic control, drawing analogies to CERN. His position: the biggest alignment challenge is not between AI and humans, but between those who control AI systems and the rest of the population. Concentrated AI development creates fragile single points of failure; distributed development is both safer and more innovative.
---
REFERENCES:
paper:
[00:03:05] Deep RL Doesn't Work Yet
alexirpan.com/2018/02/14/rl-hard.html
[00:06:10] JaxMARL
arxiv.org/html/2311.10090v5
[00:08:50] M-FOS: Model-Free Opponent Shaping
arxiv.org/abs/2205.01447
[00:12:10] Kinetix Physics Simulator
arxiv.org/abs/2410.23208
[00:14:42] Mirror Learning Framework
arxiv.org/abs/2208.01682
[00:16:30] Discovered Policy Optimisation
arxiv.org/abs/2210.05639
[00:28:55] AlphaGo
arxiv.org/abs/1712.01815
[00:41:00] Open Source Generative AI
arxiv.org/abs/2405.08597
tool:
[00:09:45] JAX Library
github.com/jax-ml/jax
[00:49:51] Llama 3
ai.meta.com/blog/meta-llama-3
concept:
[00:24:10] Goodhart's Law
en.wikipedia.org/wiki/Goodhart%27s_law
---
LINKS:
Full Transcript: app.rescript.info/share/04498ba49b081dbcc254c40ba7b56035
Download PDF transcript: app.rescript.info/api/public/sessions/8fe06defb6d2216d/pdf
Prof. Jakob Foerster
https://x.com/j_foerst
jakobfoerster.com
University of Oxford Profile:
eng.ox.ac.uk/people/jakob-foerster
REFS
[[00:00:05] ARC Benchmark, Chollet
github.com/fchollet/ARC-AGI
[00:09:45] JAX Library, Google Research
github.com/jax-ml/jax
[00:25:15] LLM ARChitect, Franzen et al.
github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!
centml.ai/pricing
Prof. Jakob Foerster (FLAIR lab, Oxford / Meta AI) and his PhD student Chris Lu make the case that deep reinforcement learning is finally winning the hardware lottery. The core thesis: RL has underperformed not because the ideas are wrong, but because running environments on CPUs while training agents on GPUs created a computational bottleneck that made experimentation slow, expensive, and brittle. JAX-based GPU-native environments now deliver ~4000x speedups, enabling the kind of rapid iteration that made supervised deep learning successful.
Chris Lu explains the technical foundation -- how JAX's JIT compilation and vmap (vectorized map) allow writing a single environment instance in NumPy-like code and scaling it to millions of parallel copies on GPU. This started from necessity: their lab initially had only Google Colab free-tier compute. The constraint forced them to put environments on GPU for their Model-Free Opponent Shaping paper, and the results were surprisingly effective.
The conversation then shifts to discovered policy optimization. Foerster's mirror learning framework provides theoretical grounding for why PPO works, and crucially, lets you parameterize the drift function as a neural network and meta-learn it. Evolution strategies, not gradient-based meta-learning, turned out to be the better optimizer for this -- a vindication of the bitter lesson. The learned policy optimization function shows intriguing "too good to be true" behavior: when advantages are very high, it clips more aggressively, as if it has learned skepticism.
The second half covers multi-agent systems, emergent communication, and AI governance. Foerster argues forcefully for open-source AI development and democratic control, drawing analogies to CERN. His position: the biggest alignment challenge is not between AI and humans, but between those who control AI systems and the rest of the population. Concentrated AI development creates fragile single points of failure; distributed development is both safer and more innovative.
---
REFERENCES:
paper:
[00:03:05] Deep RL Doesn't Work Yet
alexirpan.com/2018/02/14/rl-hard.html
[00:06:10] JaxMARL
arxiv.org/html/2311.10090v5
[00:08:50] M-FOS: Model-Free Opponent Shaping
arxiv.org/abs/2205.01447
[00:12:10] Kinetix Physics Simulator
arxiv.org/abs/2410.23208
[00:14:42] Mirror Learning Framework
arxiv.org/abs/2208.01682
[00:16:30] Discovered Policy Optimisation
arxiv.org/abs/2210.05639
[00:28:55] AlphaGo
arxiv.org/abs/1712.01815
[00:41:00] Open Source Generative AI
arxiv.org/abs/2405.08597
tool:
[00:09:45] JAX Library
github.com/jax-ml/jax
[00:49:51] Llama 3
ai.meta.com/blog/meta-llama-3
concept:
[00:24:10] Goodhart's Law
en.wikipedia.org/wiki/Goodhart%27s_law
---
LINKS:
Full Transcript: app.rescript.info/share/04498ba49b081dbcc254c40ba7b56035
Download PDF transcript: app.rescript.info/api/public/sessions/8fe06defb6d2216d/pdf
Prof. Jakob Foerster
https://x.com/j_foerst
jakobfoerster.com
University of Oxford Profile:
eng.ox.ac.uk/people/jakob-foerster
REFS
[[00:00:05] ARC Benchmark, Chollet
github.com/fchollet/ARC-AGI
[00:09:45] JAX Library, Google Research
github.com/jax-ml/jax
[00:25:15] LLM ARChitect, Franzen et al.
github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf
![ImageNet Moment for Reinforcement Learning? [Prof. Jakob Foerster]](https://i.ytimg.com/vi/Jr_nGkCG3og/mqdefault.jpg "ImageNet Moment for Reinforcement Learning? [Prof. Jakob Foerster]
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!
https://centml.ai/pricing/
Prof. Jakob Foerster (FLAIR lab, Oxford / Meta AI) and his PhD student Chris Lu make the case that deep reinforcement learning is finally winning the hardware lottery. The core thesis: RL has underperformed not because the ideas are wrong, but because running environments on CPUs while training agents on GPUs created a computational bottleneck that made experimentation slow, expensive, and brittle. JAX-based GPU-native environments now deliver ~4000x speedups, enabling the kind of rapid iteration that made supervised deep learning successful.
Chris Lu explains the technical foundation how JAXs JIT compilation and vmap (vectorized map) allow writing a single environment instance in NumPy-like code and scaling it to millions of parallel copies on GPU. This started from necessity: their lab initially had only Google Colab free-tier compute. The constraint forced them to put environments on GPU for their Model-Free Opponent Shaping paper, and the results were surprisingly effective.
The conversation then shifts to discovered policy optimization. Foersters mirror learning framework provides theoretical grounding for why PPO works, and crucially, lets you parameterize the drift function as a neural network and meta-learn it. Evolution strategies, not gradient-based meta-learning, turned out to be the better optimizer for this a vindication of the bitter lesson. The learned policy optimization function shows intriguing too good to be true behavior: when advantages are very high, it clips more aggressively, as if it has learned skepticism.
The second half covers multi-agent systems, emergent communication, and AI governance. Foerster argues forcefully for open-source AI development and democratic control, drawing analogies to CERN. His position: the biggest alignment challenge is not between AI and humans, but between those who control AI systems and the rest of the population. Concentrated AI development creates fragile single points of failure; distributed development is both safer and more innovative.
REFERENCES:
paper:
[00:03:05] Deep RL Doesnt Work Yet
https://www.alexirpan.com/2018/02/14/rl-hard.html
[00:06:10] JaxMARL
https://arxiv.org/html/2311.10090v5
[00:08:50] M-FOS: Model-Free Opponent Shaping
https://arxiv.org/abs/2205.01447
[00:12:10] Kinetix Physics Simulator
https://arxiv.org/abs/2410.23208
[00:14:42] Mirror Learning Framework
https://arxiv.org/abs/2208.01682
[00:16:30] Discovered Policy Optimisation
https://arxiv.org/abs/2210.05639
[00:28:55] AlphaGo
https://arxiv.org/abs/1712.01815
[00:41:00] Open Source Generative AI
https://arxiv.org/abs/2405.08597
tool:
[00:09:45] JAX Library
https://github.com/jax-ml/jax
[00:49:51] Llama 3
https://ai.meta.com/blog/meta-llama-3/
concept:
[00:24:10] Goodharts Law
https://en.wikipedia.org/wiki/Goodhart%27s_law
LINKS:
Full Transcript: https://app.rescript.info/share/04498ba49b081dbcc254c40ba7b56035
Download PDF transcript: https://app.rescript.info/api/public/sessions/8fe06defb6d2216d/pdf
Prof. Jakob Foerster
https://x.com/j_foerst
https://www.jakobfoerster.com/
University of Oxford Profile:
https://eng.ox.ac.uk/people/jakob-foerster/
REFS
[[00:00:05] ARC Benchmark, Chollet
https://github.com/fchollet/ARC-AGI
[00:09:45] JAX Library, Google Research
https://github.com/jax-ml/jax
[00:25:15] LLM ARChitect, Franzen et al.
https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf")
![AI Isnt Creative [Prof. Kenneth Stanley]](https://i.ytimg.com/vi/KKUKikuV58o/mqdefault.jpg "AI Isnt Creative [Prof. Kenneth Stanley]
Are the AI models you use today imposters?
Please watch the intro video we did before this: https://www.youtube.com/watch?v=o1q6Hhz0MAg
In this episode, hosts Dr. Tim Scarfe and Dr. Duggar are joined by AI researcher Prof. Kenneth Stanley and MIT PhD student Akash Kumar to discuss their fascinating paper, Questioning Representational Optimism in Deep Learning.
Imagine you ask two people to draw a perfect skull. One is a brilliant artist who understands anatomy, the other is a machine that just traces the image. Both drawings look identical, but the artist understands what a skull is—they know where the mouth is, how the jaw works, and that its symmetrical. The machine just has a tangled mess of lines that happens to form the right picture.
An AI with an elegant representation, has the building blocks to generate truly new ideas.
The Path Is the Goal: As Kenneth Stanley puts it, it matters not just where you get, but how you got there. Two students can ace a math test, but the one who truly understands the concepts—instead of just memorizing formulas—is the one who will go on to make new discoveries.
The show is a mixture of 3 separate recordings we have done, the original Patreon warmup with Tim/Kenneth, the Tim/Keith Steakhouse recorded after the main interview, then the main interview with Kenneth/Akarsh/Keith/Tim. Feel free to skip around. We had to edit this in a rush as we are travelling next week but its reasonably cleaned up.
DUPLICATION NOTE:
There is a little bit of content duplication/overlap in these segments, if you are time-limited and dont want duplication - just watch the main interview from 48 mins
TOC:
00:00:00 Intro: Garbage vs. Amazing Representations
00:05:42 How Good Representations Form
00:11:14 Challenging the Bitter Lesson
00:18:04 AI Creativity & Representation Types
00:22:13 Steakhouse: Critiques & Alternatives
00:28:30 Steakhouse: Key Concepts & Goldilocks Zone
00:39:42 Steakhouse: A Sober View on AI Risk
00:43:46 Steakhouse: The Paradox of Open-Ended Search
00:47:58 Main Interview: Paper Intro & Core Concepts
00:56:44 Main Interview: Deception and Evolvability
01:36:30 Main Interview: Reinterpreting Evolution
01:56:16 Main Interview: Impostor Intelligence
02:11:15 Main Interview: Recommendations for AI Research
REFS:
Questioning Representational Optimism in Deep Learning:
The Fractured Entangled Representation Hypothesis
Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley
https://arxiv.org/pdf/2505.11581
Kenneth O. Stanley, Joel Lehman
Why Greatness Cannot Be Planned: The Myth of the Objective
https://amzn.to/44xLaXK
Original show with Kenneth from 4 years ago:
https://www.youtube.com/watch?v=lhYGXYeMq_E
Kenneth Stanley is SVP Open Endedness at Lila Sciences
https://x.com/kenneth0stanley
Akarsh Kumar (MIT)
https://akarshkumar.com/
AND... Kenneth is HIRING (this is an OPPORTUNITY OF A LIFETIME!)
Research Engineer: https://job-boards.greenhouse.io/lila/jobs/7890007002
Research Scientist: https://job-boards.greenhouse.io/lila/jobs/8012245002
TRANSCRIPT:
https://app.rescript.info/public/share/yVOfAlH9dwWoRozJGYrDiYSUlMFtRkzFPMW2S-9OQlI")
![The Real Reason Huge AI Models Actually Work [Prof. Andrew Wilson]](https://i.ytimg.com/vi/M-jTeBCEGHc/mqdefault.jpg "The Real Reason Huge AI Models Actually Work [Prof. Andrew Wilson]
Why can billion-parameter models perform so well without catastrophically overfitting? The answer lies in the mysterious simplicity bias that emerges at scale, a core concept of the double descent phenomenon.
Professor Andrew Wilson from NYU explains why many common-sense ideas in artificial intelligence might be wrong. For decades, the rule of thumb in machine learning has been to fear complexity. The thinking goes: if your model has too many parameters (is too complex) for the amount of data you have, it will overfit by essentially memorizing the data instead of learning the underlying patterns. This leads to poor performance on new, unseen data. This is known as the classic bias-variance trade-off i.e. a balancing act between a model thats too simple and one thats too complex.
**SPONSOR MESSAGES**
—
Tufa AI Labs is an AI research lab based in Zurich. **They are hiring ML research engineers!**
This is a once in a lifetime opportunity to work with one of the best labs in Europe
Contact Benjamin Crouzier - https://tufalabs.ai/
—
Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!
—
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Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++
Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst
Submit investment deck: https://cyber.fund/contact?utm_source=mlst
—
Description Continued:
Professor Wilson challenges this fundamental belief (fearing complexity). He makes a few surprising points:
**Bigger Can Be Better**: massive models dont just get more flexible; they also develop a stronger simplicity bias. So, if your model is overfitting, the solution might paradoxically be to make it even bigger.
**The Bias-Variance Trade-off is a Misnomer**: Wilson claims you dont actually have to trade one for the other. You can have a model that is incredibly expressive and flexible while also being strongly biased toward simple solutions. He points to the double descent phenomenon, where performance first gets worse as models get more complex, but then surprisingly starts getting better again.
**Honest Beliefs and Bayesian Thinking**: His core philosophy is that we should build models that honestly represent our beliefs about the world. We believe the world is complex, so our models should be expressive. But we also believe in Occams razor—that the simplest explanation is often the best. He champions Bayesian methods, which naturally balance these two ideas through a process called marginalization, which he describes as an automatic Occams razor.
TOC:
[00:00:00] Introduction and Thesis
[00:04:19] Challenging Conventional Wisdom
[00:11:17] The Philosophy of a Scientist-Engineer
[00:16:47] Expressiveness, Overfitting, and Bias
[00:28:15] Understanding, Compression, and Kolmogorov Complexity
[01:05:06] The Surprising Power of Generalization
[01:13:21] The Elegance of Bayesian Inference
[01:33:02] The Geometry of Learning
[01:46:28] Practical Advice and The Future of AI
Prof. Andrew Gordon Wilson:
https://x.com/andrewgwils
https://cims.nyu.edu/~andrewgw/
https://scholar.google.com/citations?user=twWX2LIAAAAJ&hl=en
https://www.youtube.com/watch?v=Aja0kZeWRy4
https://www.youtube.com/watch?v=HEp4TOrkwV4
TRANSCRIPT:
https://app.rescript.info/public/share/H4Io1Y7Rr54MM05FuZgAv4yphoukCfkqokyzSYJwCK8
REFS:
Deep Learning is Not So Mysterious or Different [Andrew Gordon Wilson]
https://arxiv.org/abs/2503.02113
Bayesian Deep Learning and a Probabilistic Perspective of Generalization [Andrew Gordon Wilson, Pavel Izmailov]
https://arxiv.org/abs/2002.08791
Compute-Optimal LLMs Provably Generalize Better With Scale [Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson]
https://arxiv.org/abs/2504.15208")
![ARC Prize Version 2 Launch Video! [Francois Chollet, Mike Knoop]](https://i.ytimg.com/vi/M3b59lZYBW8/mqdefault.jpg "ARC Prize Version 2 Launch Video! [Francois Chollet, Mike Knoop]
Francois Chollet and Mike Knoop join Tim Scarfe to announce ARC-AGI 2 and the ARC Prize 2025 contest. The new benchmark has been human-calibrated with 400 participants and adversarially designed so that frontier reasoning models score in the single digits, while every task remains solvable by at least two humans. The conversation covers the story behind testing OpenAI o3 on ARC v1 (where it scored 75-85%), what those results reveal about fluid intelligence versus brute-force capability, and why Chollet considers o3 the first model with genuine if limited fluid intelligence. Technical discussion ranges from chain-of-thought reasoning as natural language program synthesis, to the exponential failure modes of current reasoning systems on spatially complex tasks, to the deeper question of whether intelligence is best understood as a binary category or a spectrum of recombination depth. Chollet argues that intelligence is fundamentally about efficiency of knowledge acquisition and adaptation, not raw capability, and that ARC-AGI 2 is designed to measure exactly this distinction.
SPONSOR MESSAGES:
***
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
REFERENCES:
Paper:
[00:00:15] On the Measure of Intelligence
https://arxiv.org/abs/1911.01547
[00:12:50] OpenAI o3 Performance on ARC v1
https://arcprize.org/blog/oai-o3-pub-breakthrough
[00:18:30] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://arxiv.org/abs/2201.11903
[00:26:05] ARC Prize 2024: Technical Report
https://arxiv.org/abs/2412.04604
[00:48:57] The Bitter Lesson
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
[00:53:30] Decoding Strategies in Neural Text Generation
https://www.mdpi.com/2078-2489/12/9/355/pdf
Organization:
[00:06:45] ARC Prize Foundation
https://arcprize.org/
LINKS:
Full Transcript: https://app.rescript.info/share/19ed0e8921636ce804d8780e6b03f67d
Download PDF transcript: https://app.rescript.info/api/public/sessions/302bc045019e056a/pdf")
![You dont fine-tune your way to AGI - Heres why. [Eiso Kant]](https://i.ytimg.com/vi/NDrosuKhXeo/mqdefault.jpg "You dont fine-tune your way to AGI - Heres why. [Eiso Kant]
Eiso Kant is the co-founder and CTO of Poolside AI, one of roughly seven companies worldwide with the technical muscle to build frontier foundation models from scratch. He sat down with Tim to explain why Poolside deliberately rejected the prevailing wisdom of just scale up the next GPT and instead bet the company on a thesis most labs were ignoring: that reinforcement learning from code execution feedback is the missing scaling axis.
The argument is straightforward and, once you hear it, hard to unsee. Next-token prediction is imitation learning. Reinforcement learning is trial-and-error learning. Poolside maintains close to a million fully containerized code repositories each with its own test suite as a massive, diverse RL environment. The model writes code, executes it, gets deterministic feedback, and learns. This is why software turns out to be the ideal domain for RL scaling: it is deterministic enough to provide clear reward signals, but diverse enough to avoid model collapse.
Along the way, Eiso and Tim get into the weeds on frontier lab operations (over 4000 experimental runs per month), why Chinchilla optimality breaks down once you account for inference cost, what DeepSeek V3 tells us about the second-generation AI company playbook, and why the R1 zero-shot reasoning result should have been the real headline rather than the dollar figure. There is a candid exchange on whether the software development lifecycle itself will progressively collapse into the model, what Chris Olah interpretability work means for alignment, and whether Karpathy was right about Software 2.0 with some important caveats Eiso has developed after a decade of building AI for code.
SPONSOR MESSAGES:
***
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
TIMESTAMPS:
00:00:00 Introduction and Guest Background
00:02:50 Poolside AIs Vision and Three-Step Plan
00:06:50 Foundation Models vs. Enterprise Customization
00:10:25 The Missing Scaling Axis: Reinforcement Learning
00:15:40 Reinforcement Learning from Code Execution Feedback
00:22:20 Model Economics and Experimental Optimization
00:26:00 Enterprise Deployment Strategy and Market Focus
00:30:30 DeepSeek, Distributed Training, and Hardware Architecture
00:36:40 Emergent Reasoning and Chain-of-Thought Scaling
00:45:00 AI-Assisted Software Development Today
00:58:20 Architecture Innovation and Model Interpretability
01:15:00 Karpathys Software 2.0 and the Future of Code
01:25:00 AWS Partnership, Enterprise Security, and Closing
REFERENCES:
website:
[00:01:40] Tufa AI Labs
https://tufalabs.ai/
[00:02:50] Poolside AI
https://poolside.ai/
social:
[00:02:50] Eiso Kant on X
https://x.com/eisokant
paper:
[00:15:40] The Curse of Recursion: Training on Generated Data Makes Models Forget
https://arxiv.org/abs/2305.17493
[00:20:00] Efficient Estimation of Word Representations in Vector Space (Word2Vec)
https://arxiv.org/abs/1301.3781
[00:22:40] On the Measure of Intelligence
https://arxiv.org/abs/1911.01547
[00:30:30] DeepSeek-V3 Technical Report
https://arxiv.org/abs/2412.19437
[00:34:30] Training Compute-Optimal Large Language Models (Chinchilla)
https://arxiv.org/abs/2203.15556
[00:45:45] The Bitter Lesson
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
[00:49:55] Mastering the Game of Go with Deep Neural Networks and Tree Search
https://www.nature.com/articles/nature16961
[01:10:15] Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://arxiv.org/abs/2312.00752
[01:14:25] Zoom In: An Introduction to Circuits
https://distill.pub/2020/circuits/zoom-in/
benchmark:
[00:46:10] ARC Prize Challenge
https://arcprize.org/
blog:
[01:19:10] Software 2.0
https://karpathy.medium.com/software-2-0-a64152b37c35
LINKS:
Full Transcript: https://app.rescript.info/share/a8144052b0a5fc7210ab37f9651a3557
Download PDF transcript: https://app.rescript.info/api/public/sessions/2f5beb6d70f79973/pdf
Eiso Kant:
https://x.com/eisokant
https://poolside.ai/")
